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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistics-probability-for-business-analytics/
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课程名称：商业分析中的统计与概率 课程概述：欢迎参加《商业分析中的统计与概率》课程。本课程是一个全面的商业数学教程，专为数据分析师和业务分析师设计。内容涵盖从基础到中级的统计和概率概念、使用Python的计算技术及其在商业分析中的实际应用。该课程完美结合了统计学和Python，是提升您的商业分析技能和统计知识的绝佳机会。 在引导课程中，您将学习商业分析、统计和概率的基本原理及其在该领域的应用，并了解商业分析工作流程。接下来，我们将开始第一课，了解描述性统计，包括均值、中位数、众数、标准差、范围、方差和四分位数。这些内容将为您总结数据、识别模式并获取有意义的商业洞察提供基础工具。 之后的第二课将介绍推断统计，内容包括假设检验、置信区间、回归分析和方差分析。这些方法将帮助您基于样本数据做出数据驱动的预测并得出结论。 第三课将覆盖概率基础，介绍基本概率计算、联合概率、条件概率、贝叶斯定理及期望值。这些概念将帮助您评估各种商业场景和结果的可能性。 在第四课中，我们将讨论概率分布，涵盖离散分布（如二项分布和泊松分布）及连续分布（如正态分布、均匀分布和指数分布）。通过学习这些概念，您将能够有效地建模不确定性、分析模式并应用概率分布解决现实中的商业问题。 第五课将侧重于相关性分析，测量变量之间的关系强度和方向。课程的最后部分，我们将应用所学的统计与概率概念，进行实际的商业案例研究，使用Python进行数据分析、建立统计模型，以及计算概率来预测客户流失。 学习统计和概率对商业分析至关重要，因为它们使企业能够有效分析数据、识别模式并更准确地理解趋势。这将帮助优化流程、预测未来结果和评估风险，确保每个决策都有数据支持。 通过本课程，您将学习到： - 商业分析及其工作流程的基础知识，统计和概率在该领域的应用 - 使用Python、Pandas、Numpy、Matplotlib、Scipy、Seaborn和Scikit Learn进行计算统计 - 描述性统计和推断统计 - 计算均值、中位数、众数、总和、最大值和最小值 - 计算标准差、方差和范围 - 将数据划分为四分位数并使用直方图可视化数据 - 进行假设检验和t检验，计算置信区间 - 使用线性回归预测房价，使用ANOVA分析价格差异 - 计算联合概率和条件概率 - 使用贝叶斯定理计算概率和期望值 - 学习离散分布和连续分布，计算二项分布、泊松分布、正态分布、均匀分布和指数分布 - 执行相关性分析并计算相关系数 - 使用逻辑回归预测客户流失 通过本课程，您将实现学习与实践相结合，提升为商业分析师所需的统计技能。
Welcome to Statistics & Probability for Business Analytics course. This is a comprehensive business math tutorial designed for data analysts and business analysts. This course will cover basic to intermediate statistics and probability concepts, computational techniques using Python and their practical applications in the field of business analytics. This course is a perfect combination between statistics and Python, making it an ideal opportunity to practice your business analytics skills while improving your statistical knowledge. In the introduction session, you will learn the basic fundamentals of business analytics, statistics and probability applications in this field, and also business analytics workflow. Then, in the next section, we will start the first lesson where you will learn about descriptive statistics. This section will cover mean, median, mode, standard deviation, range, variance, and quartile. These concepts will provide you with the foundational tools to summarize data, identify patterns, and gain meaningful business insights. Afterward, in the second lesson, you will learn about inferential statistics. This section will cover hypothesis testing, confidence interval, regression analysis, and analysis of variance. These methods will help you to make data driven predictions and draw conclusions from sample data. Then, in the third lesson, we will cover the fundamentals of probability. This section will introduce key concepts such as basic probability calculation, joint probability, conditional probability, Bayes' theorem, and expected value. These probability concepts will assist you to assess the likelihood of various business scenarios and outcomes. Meanwhile, in the fourth lesson, we will learn about probability distribution. This section will cover discrete distributions such as Binomial and Poisson, as well as continuous distributions including Normal, Uniform, and Exponential. Additionally, we will explore their practical applications in business analytics. By learning these concepts, you will be able to model uncertainty, analyze patterns, and apply probability distributions to solve real-world business problems effectively. Then, in the fifth lesson, we will learn about correlation analysis, specifically, we will measure the strength and direction of relationships between variables. At the end of the course, we will apply all statistics and probability concepts that we have learnt to real-world business case studies, where we will use Python to perform data analysis, build statistical models, and calculate probability to predict customer churn.First of all, before getting into the course, we need to ask ourselves these questions, why should we learn about statistics and probability? Why are they crucial for business analytics? Well, here is my answer, statistics and probability enable businesses to analyze data effectively, identify patterns, and understand trends with greater accuracy. They help in optimizing processes, forecasting future outcomes, and evaluating risks, ensuring that every decision is backed by evidence. By applying these concepts, businesses can enhance efficiency, improve strategies, and make better data driven decisions.Below are things that you can expect to learn from this course:Learn the basic fundamentals of business analytics, its workflow, statistics and probability applications in this fieldLearn about computational statistics using Python, Pandas, Numpy, Matplotlib, Scipy, Seaborn, and Scikit LearnLearn about descriptive statistics and inferential statisticsLearn how to calculate mean, median, mode, sum, max, and minLearn how to calculate standard deviation, variance, and rangeLearn how to split data into quartiles and visualise data using histogramLearn how to conduct hypothesis testing & t-testLearn how to calculate confidence intervalLearn how to predict house prices using linear regressionLearn how to analyze price differences using ANOVALearn how to calculate joint probability and conditional probabilityLearn how to calculate probability using Bayes TheoremLearn how to calculate expected valueLearn about discrete distribution and continuous distributionLearn how to calculate binomial distribution and poisson distributionLearn how to calculate normal distribution, uniform distribution, and exponential distributionLearn how to perform correlation analysis and calculate correlation coefficientLearn how to predict customer churn using logistic regression
